विधियों की तुलना करें

	माध्य निरपेक्ष त्रुटि (MAE)	माध्य वर्ग त्रुटि (MSE)
क्षेत्र	मॉडल मूल्यांकन	मॉडल मूल्यांकन
परिवार	MCDM	MCDM
उद्भव वर्ष	1799	1809
प्रवर्तक	Pierre-Simon Laplace	Carl Friedrich Gauss
प्रकार	Robust distance-based metric	Squared-error loss function
मौलिक स्रोत	Laplace, P. S. (1799). Traité de Mécanique Céleste. Paris: J.B.M. Duprat.	Gauss, C. F. (1809). Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium. Hamburg: Perthes and Besser.
उपनाम	MAE, L1 error, mean absolute deviation	MSE, L2 error, quadratic error
संबंधित	3	4
सारांश	Mean Absolute Error is a robust metric that measures the average absolute magnitude of prediction errors in regression models. Dating back to Pierre-Simon Laplace's work on observational errors (1799), MAE quantifies typical prediction deviation by averaging the absolute differences between observed and predicted values.	Mean Squared Error is the foundational loss function for regression models, measuring the average squared deviation between predictions and observations. Originating from Gauss and Legendre's method of least squares (1805-1809), MSE is the basis for ordinary least squares regression and remains central to modern machine learning optimization.